Abstract: Coal mine safety production video analysis and recognition technology is a core technical support for ensuring the intelligent construction of coal mines and the high-quality development of the coal industry in China. To effectively address the impact of complex underground environments such as low illumination, high dust, and non-uniform lighting on the quality of video surveillance images, and to improve the real-time performance and accuracy of safety hazard identification, image enhancement technology has become a key link in the process of coal mine video AI recognition. This paper systematically elaborates on the urgent needs and development status of image enhancement technology in the context of intelligent coal mine construction, analyzes the multi-factor coupling causes of underground image degradation and their constraints on intelligent analysis performance, and proposes an image enhancement technology system framework that runs through all levels of the system in combination with the “perception-edge-cloud” 3-level collaborative intelligent video system architecture for coal mines. Focusing on typical underground imaging challenges, it sorts out and reviews the principles, advantages, disadvantages, and representative models of traditional image enhancement methods such as histogram equalization, wavelet transform, and Retinex, deep learning-based enhancement methods such as super-resolution reconstruction, low-light enhancement, and defogging and dust removal, as well as multi-modal fusion enhancement technologies that integrate infrared/laser/millimeter wave with visible light, clarifying the technical characteristics and applicable scenarios of various methods. At the same time, combined with typical application scenarios of “human-machine-environment-management” such as mine personnel monitoring, equipment status monitoring, and operation process supervision, it demonstrates the practical application effects of targeted image enhancement technologies in improving target recognition, defect detection accuracy, and operation monitoring clarity. Finally, in view of the existing problems in current technologies, such as insufficient environmental dynamic adaptation capability, limited edge computing power, lack of high-quality real datasets, and limited processing effects on multi-factor coupling degradation, it points out the future development directions of image enhancement technology, including collaborative optimization of lightweight edge computing models and hardware, continuous deepening of research on enhancement algorithms based on advanced network architectures such as GAN and Transformer, exploring the combination with large models to achieve active intelligent perception and semantic understanding, promoting the engineering application of cross-modal fusion technology, and ultimately forming a robust visual enhancement capability to support high-precision intelligent perception of the entire “human-machine-environment” domain and collaborative management and control of hazard sources underground.